About Us

We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.

Role Overview

We're seeking a strong generalist Software Engineer (Backend) to build the production systems and customer integrations that bring our models into clinical use at scale. On a lean engineering team, you'll wear several hats: designing and shipping backend services and APIs, building the networking and integration layer that connects us securely to customer hospital systems, standing up production data pipelines and CI/CD, and keeping all of it HIPAA-compliant. You'll work closely with the ML team, lending a hand on ML infrastructure when priorities demand, and you'll do well here if you like owning ambiguous, high-impact problems end to end.

Key Responsibilities

• Design, build, and operate backend services and APIs that deliver reliable end-to-end experiences from data ingestion through model output.

• Build and maintain the integration layer connecting our applications to customer systems, including PACS, RIS, and EHRs, using medical communication protocols such as DICOM, DICOMweb, HL7, and FHIR.

• Design reliable cloud networking for secure customer onboarding, including site-to-site VPNs and connectivity that scales to many external sites, and work directly with customer IT teams to bring each site online.

• Build and operate production data pipelines and storage, spanning ETL, data lake, and data warehouse, handling hundreds of terabytes of imaging and clinical data.

• Build CI/CD and deployment automation that lets a lean team ship quickly and safely.

• Ensure systems handling PHI are HIPAA-compliant by design, with encryption, access controls, audit logging, and secure data handling throughout.

• Build model inference pipelines for live and offline traffic, and support ML infrastructure alongside the ML team as needs arise.

• Own large backend projects from design through deployment, leading technical design, driving quality through design reviews and testing, and helping set engineering best practices on a lean team.
  

Qualifications

• 5+ years as a software engineer, with a track record of delivering on time and at quality

• Proficiency in a backend language (Go, Python, Java, or similar) and strong experience with cloud platforms and distributed architectures, particularly AWS

• Strong experience designing and building backend infrastructure, including APIs, data pipelines, and services, that is reliable, scalable, and maintainable

• Hands-on experience with schema design and data modeling

• Experience building and integrating with external or third-party systems, including the networking and security concerns of connecting across organizational boundaries

• Strong problem-solving skills and the ability to troubleshoot complex distributed systems

• Experience shipping quickly under competing priorities, and comfort with the ambiguity of a lean team

• Excellent communication skills and the ability to work cross-functionally with technical and non-technical stakeholders, including external customers and vendors
  

Preferred Qualifications

• Experience with healthcare integration standards and imaging systems (DICOM, DICOMweb, HL7, FHIR, PACS/RIS, IHE profiles)

• Experience building and operating HIPAA-compliant systems, or handling PHI and healthcare security requirements

• Experience with secure network integration, such as site-to-site VPNs, private connectivity, and onboarding enterprise or hospital customers alongside their IT teams

• Experience with CI/CD tooling and infrastructure-as-code (Terraform, Docker, Kubernetes)

• Experience with large-scale ETL, data lake, or data warehouse systems

• Experience contributing to ML infrastructure or model inference pipelines